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Probability and Computing

Probability and Computing

Randomized Algorithms and Probabilistic Analysis

by Michael Mitzenmacher and Eli Upfal
Hardback
Publication Date: 31/01/2005

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$135.00
Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, balls and bins models, the probabilistic method, and Markov chains. In the second half, the authors delve into more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods, coupling, martingales, and balanced allocations.
With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.
ISBN:
9780521835404
9780521835404
Category:
Numerical analysis
Format:
Hardback
Publication Date:
31-01-2005
Language:
English
Publisher:
Cambridge University Press
Country of origin:
United Kingdom
Pages:
370
Dimensions (mm):
260x183x25mm
Weight:
0.81kg

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